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Data Science for Decision Makers

Data Science for Decision Makers

By : Howells
4.8 (5)
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Data Science for Decision Makers

Data Science for Decision Makers

4.8 (5)
By: Howells

Overview of this book

As data science and artificial intelligence (AI) become prevalent across industries, executives without formal education in statistics and machine learning, as well as data scientists moving into leadership roles, must learn how to make informed decisions about complex models and manage data teams. This book will elevate your leadership skills by guiding you through the core concepts of data science and AI. This comprehensive guide is designed to bridge the gap between business needs and technical solutions, empowering you to make informed decisions and drive measurable value within your organization. Through practical examples and clear explanations, you'll learn how to collect and analyze structured and unstructured data, build a strong foundation in statistics and machine learning, and evaluate models confidently. By recognizing common pitfalls and valuable use cases, you'll plan data science projects effectively, from the ground up to completion. Beyond technical aspects, this book provides tools to recruit top talent, manage high-performing teams, and stay up to date with industry advancements. By the end of this book, you’ll be able to characterize the data within your organization and frame business problems as data science problems.
Table of Contents (20 chapters)
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1
Part 1: Understanding Data Science and Its Foundations
7
Part 2: Machine Learning – Concepts, Applications, and Pitfalls
13
Part 3: Leading Successful Data Science Projects and Teams

Introducing Machine Learning

In the rapidly evolving world of data-driven decision-making, machine learning has emerged as a game-changer. As a decision-maker, understanding the fundamentals of this powerful tool is essential to leverage its potential and stay ahead in today’s competitive landscape.

In this chapter, we will demystify machine learning, exploring its roots in statistics and its pivotal role within artificial intelligence (AI). We’ll break down the different types of machine learning techniques and walk through the process of training, validating, and testing models. Moreover, we’ll dive into the exciting world of deep learning and its transformative impact on various industries.

By the end of this chapter, you’ll have a solid grasp of the following topics:

  • The evolution from traditional statistics to machine learning
  • The significance of machine learning in data-driven decision-making
  • The diverse applications of machine...
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Data Science for Decision Makers
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